H. V. D. Parunak

Swarming Polyagents Executing Hierarchical Task Networks

Swarming agents often operate in benign geographic topologies that let them explore alternative trajectories with minor variations that the agent dynamics then amplify for improved performance. In this paper we demonstrate the deployment of swarming agents in the non-metric and discontinuous topology of a process graph. We align our research with traditional AI approaches and focus on Hierarchical Task Network (HTN) descriptions of constraints and preferences in the execution of abstract methods by a group of real-world entities. In particular, we adapt the TAEMS representation to place a greater emphasis on the mediation of method-execution through shared resources and collectively achieved quality (stigmergic coordination). The paper presents our polyagent model and experiments that demonstrate the scalability of the system and the ability of our agents to achieve optimal entity coordination.

Sift and sort: climbing the semantic pyramid

Information processing operations in support of intelligence analysis are of two kinds. They may sift relevant data from a larger body, thus reducing its quantity, or sort that data, thus reducing its entropy. These two classes of operation typically alternate with one another, successively shrinking and organizing the available data to make it more accessible and understandable. We term the resulting construct, the “semantic pyramid.” We sketch the general structure of this construct, and illustrate two adjacent layers of it that we have implemented in the Ant CAFE.

Applying Metareasoning to Swarming Systems: Approaches and Architectures

Metareasoning is defined as the application of reasoning techniques to the process of reasoning itself. As such, it is not immediately concerned with the particular domain of an application, but with the decision processes and knowledge representations that provide the application functionality. A primary reason to perform metareasoning is the desire to reflect on the application’s performance and adapt its reasoning processes when needed. Research in metareasoning typically assumes that the domain reasoning is performed by a single process with symbolic knowledge representations and that both the process and the knowledge are accessible to the metareasoning component. Self-organizing systems with emergent properties stand in stark contrast to this assumed architecture. There, the application function is not explicitly encoded into a single entity but it emerges dynamically from complex interactions of simple agents. In this position paper we discuss the challenge of metareasoning in swarming systems and present three examples from past work that suggest possible approaches.